Biology subjects

Rasmussen, K. D.

Publications and source records attributed to Rasmussen, K. D..

4 recordsLinked to original sources

bioRxiv2024

PROSER1 Modulates DNA Demethylation through Dual Mechanisms to Prevent Syndromic Developmental Malformations

The link between DNA methylation and neurodevelopmental disorders is well established. However, how DNA methylation is fine-tuned - ensuring precise gene expression and developmental fidelity - remains poorly understood. PROSER1, a known TET2 interactor, was recently linked to a severe neurodevelopmental disorder. Here, we demonstrate that PROSER1 interacts with all TET enzymes and stabilizes chromatin-bound TET-OGT-PROSER1-DBHS (TOPD) complexes, which regulate DNA demethylation and developmental gene expression. Surprisingly, we find that PROSER1 also sequesters TET enzymes, preventing widespread demethylation and transposable element de-repression. Our findings identify PROSER1 as a key factor which both positively and negatively regulates DNA demethylation essential for mammalian neurodevelopment.

developmental biology↗

bioRxiv2023

Rewiring of the TCR signalosome in natural intestinal Intraepithelial T lymphocytes drives non-deletional tolerance

Natural intraepithelial T lymphocytes (T-IELs) are innate-like, intestine-resident T cells essential for gut homeostasis. These cells express self-reactive T cell antigen receptors (TCRs) due to thymic agonist selection, but they do not cause autoimmunity. The mechanism underlying natural T-IELs tolerance in the gut is unclear. Using TCR reporter mouse models and phosphoproteomics, we demonstrate that TCR signaling is intrinsically suppressed in natural T-IELs. We discover that this suppression occurs post-selection in the thymus through altered expression of TCR signalosome components, a mechanism we term RePrESS (Rewiring of Proximal Elements of TCR Signalosome for Suppression). RePrESS is evolutionarily conserved and also found in autoreactive innate-like T cells from skin, breast and prostate. In coeliac disease, tolerance breakdown is associated with loss of RePrESS in natural T-IELs. Our findings reveal a distinct, conserved mechanism of tolerance involving TCR signaling rewiring, with implications for understanding barrier tissue homeostasis and autoimmune disease. One sentence summaryAutoreactive innate-like T lymphocytes are developmentally suppressed by rewiring of the T cell antigen receptor signaling pathway to maintain tolerance.

immunology↗

bioRxiv2021

COMPARE, an ultra-fast and robust suite for multiparametric screening, identifies phenotypic drug responses in acute myeloid leukemia

Multiparametric phenotypic screening of cells, for example assessing their responses to small molecules or knockdown/knockout of specific genes, is a powerful approach to understanding cellular systems and identifying potential new therapeutic strategies. However, automated tools for analyzing similarities and differences between a large number of tested conditions have not been readily available. Methods designed for clustering cells cannot identify differences between samples effectively. We introduce O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW for ultra-fast and robust analysis of multiparametric high-throughput screening. Applying a mass-aware gridding algorithm using hypercubes, O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW performs automatic and effective similarity comparison for hundreds to thousands of tests and provides information about the treatment effect. Particularly for screening data, O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW is equipped with modules to remove various sources of bias. Benchmarking tests show that O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW can circumvent batch effects and perform a similarity analysis substantially faster than conventional analysis tools. Applying O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW to high-throughput flow cytometry screening data, we were able to distinguish subtle phenotypic drug responses in a human sample and a genetically engineered mouse model with acute myeloid leukemia (AML). O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW revealed groups of drugs with similar responses even though their mechanisms are distinct from each other. In another screening, O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW effectively circumvented batch effects and grouped samples from AML and myelodysplastic syndrome (MDS) patients using clinical flow cytometry data.

cancer biology↗

bioRxiv2018

Quantification of differential transcription factor activity and multiomic-based classification into activators and repressors: diffTF

Transcription factor (TF) activity is an important read-out of cellular signalling pathways and thus to assess regulatory differences across conditions. However, current technologies lack the ability to simultaneously assess activity changes for multiple TFs and in particular to determine whether a specific TF acts globally as transcriptional repressor or activator. To this end, we introduce a widely applicable genome-wide method diffTF to assess differential TF activity and to classify TFs as activator or repressor (available at https://git.embl.de/grp-zaugg/diffTF). This is done by integrating any type of genome-wide chromatin accessibility data with RNA-Seq data and in-silico predicted TF binding sites. We corroborated the classification of TFs into repressors and activators by three independent analyses based on enrichments of active/repressive chromatin states, correlation of TF activity with gene expression, and activator-and repressor-specific chromatin footprints. To show the power of diffTF, we present two case studies: First, we applied diffTF in to a large ATAC-Seq/RNA-Seq dataset comparing mutated and unmutated chronic lymphocytic leukemia samples, where we identified dozens of known (40%) and potentially novel (60%) TFs that are differentially active. We were also able to classify almost half of them as either repressor and activator. Second, we applied diffTF to a small ATAC-Seq/RNA-Seq data set comparing two cell types along the hematopoietic differentiation trajectory (multipotent progenitors - MPP - versus granulocyte-macrophage progenitors - GMP). Here we identified the known drivers of differentiation and found that the majority of the differentially active TFs are transcriptional activators. Overall, diffTF was able to recover the known TFs in both case studies, additionally identified TFs that have been less well characterized in the given condition, and provides a classification of the TFs into transcriptional activators and repressors.

bioinformatics↗